AbRSA: A robust tool for antibody numbering

Identification of Immunodominant Epitopes of Dengue Virus 2 Envelope and NS1 Proteins: Evaluating the Diagnostic Potential of a Synthetic Peptide

BACKGROUND AND OBJECTIVE

Dengue is a major infectious disease with potential for outbreaks and epidemics. A specific and sensitive diagnosis is a prerequisite for clinical management of the disease. We designed our study to identify epitopes on the Dengue virus (DENV) envelope (E) and non-structural protein 1 (NS1) with potential for diagnosis.

METHODS

Serology and immunoinformatic approaches were employed. We collected DENV-positive, DENV-negative and Japanese encephalitis virus-positive samples from collaborating hospitals in 2019 and 2022-2023. Seropositive peptides in 15-18 mer peptide arrays of E and NS1 proteins of DENV2 were determined by an indirect enzyme-linked immunosorbent assay. B-cell linear and conformational epitopes were predicted using BepiPred2.0 and ElliPro, respectively. A consensus recombinant peptide was designed, synthesised and evaluated for its diagnostic potential using patient sera.

RESULTS

Eight peptides of E protein and six peptides of NS1 protein were identified to be the most frequently recognised by Dengue-positive patients. These peptide sequences were compared with B-cell epitope regions and found to be overlapped with predicted B-cell linear and conformational epitopes. EP11 and NSP15 showed a 100% amino acid sequence overlap with B-cell epitopes. EP1 and NSP15 had 14 whereas EP28, EP31, EP60 16, NSP12 and NSP32 had more than 15 interacting interface residues with a neutralising antibody, suggesting a strength of interaction. Interestingly, potential epitopes identified were localised on the surface of proteins as visualised by PyMOL. Validation with a recombined synthetic peptide yielded 92.3% sensitivity and 91.42% specificity.

CONCLUSIONS

Immunodominant regions identified by serology and computationally predicted epitopes overlapped, thereby showing the robustness of the methodology and the peptide designed for diagnosis.

Functional and structural basis of human parainfluenza virus type 3 neutralization with human monoclonal antibodies

Human parainfluenza virus type 3 (hPIV3) is a respiratory pathogen that can cause severe disease in older people and infants. Currently, vaccines against hPIV3 are in clinical trials but none have been approved yet. The haemagglutinin-neuraminidase (HN) and fusion (F) surface glycoproteins of hPIV3 are major antigenic determinants. Here we describe naturally occurring potently neutralizing human antibodies directed against both surface glycoproteins of hPIV3. We isolated seven neutralizing HN-reactive antibodies and a pre-fusion conformation F-reactive antibody from human memory B cells. One HN-binding monoclonal antibody (mAb), designated PIV3-23, exhibited functional attributes including haemagglutination and neuraminidase inhibition. We also delineated the structural basis of neutralization for two HN and one F mAbs. MAbs that neutralized hPIV3 in vitro protected against infection and disease in vivo in a cotton rat model of hPIV3 infection, suggesting correlates of protection for hPIV3 and the potential clinical utility of these mAbs.

A comprehensive immunoinformatics study to explore and characterize potential vaccine constructs against Ole e 9 allergen of Olea europaea

Immunoglobulin E (IgE)-mediated allergy, which affects more than 30% of the population, is the most prevalent hypersensitivity illness. In an atopic individual, even a small amount of allergen exposure can cause IgE antibodies to be produced. Due to the engagement of receptors that are highly selective for IgE, even tiny amounts of allergens can induce massive inflammation. This study focuses on the exploration and characterization of the allergen potential of Olea europaea allergen (Ole e 9) affecting the population in Saudi Arabia. A systematic computational approach was performed to identify potential epitopes of allergens and complementary determining regions of IgE. In support, physiochemical characterization and secondary structure analysis unravel the structural conformations of allergens and active sites. Epitope prediction uses a pool of computational algorithms to identify plausible epitopes. Furthermore, the vaccine construct was assessed for its binding efficiency using molecular docking and molecular dynamics simulation studies, which led to strong and stable interactions. This is because IgE is known to play a role in allergic responses, which facilitate host cell activation for an immune response. Overall, the immunoinformatics analysis advocates that the proposed vaccine candidate is safe and immunogenic and therefore can be pushed as a lead for in vitro and in vivo investigations.Communicated by Ramaswamy H. Sarma.

For antibody sequence generative modeling, mixture models may be all you need

MOTIVATION

Antibody therapeutic candidates must exhibit not only tight binding to their target but also good developability properties, especially low risk of immunogenicity.

RESULTS

In this work, we fit a simple generative model, SAM, to sixty million human heavy and seventy million human light chains. We show that the probability of a sequence calculated by the model distinguishes human sequences from other species with the same or better accuracy on a variety of benchmark datasets containing >400 million sequences than any other model in the literature, outperforming large language models (LLMs) by large margins. SAM can humanize sequences, generate new sequences, and score sequences for humanness. It is both fast and fully interpretable. Our results highlight the importance of using simple models as baselines for protein engineering tasks. We additionally introduce a new tool for numbering antibody sequences which is orders of magnitude faster than existing tools in the literature.

AVAILABILITY AND IMPLEMENTATION

All tools developed in this study are available at https://github.com/Wang-lab-UCSD/AntPack.

Towards a better understanding of light-glucose induced modifications on the structure and biological activity of formulated Nivolumab

In the last years, monoclonal antibodies (mAbs) have rapidly escalated as biopharmaceuticals into cancer treatments, mainly for their target specificity accompanied by less side effects than the traditional chemotherapy, and stimulation of reliable long-term anti-tumoral responses. They are potentially unstable macromolecules under shaking, temperature fluctuations, humidity, and indoor and outdoor light exposure, all stressors occurring throughout their production, transport, storage, handling, and administration steps. The chemical and physical modifications of mAbs can lead not only to the loss of their bioactivity, but also to the enhancement of their immunogenicity with increasing risk of severe hypersensitivity reactions in treated patients because of aggregation. The photostability of Nivolumab, the active principle of Opdivo®, has been here studied. The chemical modifications detected by LC-MS/MS after the light stressor showed Trp and Met mono and double oxidations as primary damage induced by light on this mAb. The oxidations were stronger when the mAb was diluted in sterile glucose solution where 5-HMF, a major heat glucose degradation product, acted as singlet oxygen producer under irradiation. However, no significant changes in the mAb conformation were found. On the contrary, formation of a significant extent of aggregates has been detected after shining high simulated sunlight doses. This again took place particularly when Nivolumab was diluted in sterile glucose, thus raising a direct correlation between the aggregation and the oxidative processes. Finally, the biological activity under light stress assessed by a blockade assay test demonstrated the maintenance of the PD-1 target recognition even under high light doses and in glucose solution, in line with the preservation of the secondary and tertiary structures of the mAb. Based on our results, as sterile glucose is mostly used for children's therapies, special warnings, and precautions for healthcare professionals should be included for their use to the pediatric population.

Preparation of Bispecific IgY-scFvs Inhibition Adherences of Enterotoxigenic Escherichia coli (K88 and F18) to Porcine IPEC-J2 Cell

Enterotoxigenic Escherichia coli (ETEC) strains are significant contributors to postweaning diarrhea in piglets. Of the ETEC causing diarrhea, K88 and F18 accounted for 92.7%. Despite the prevalence of ETEC K88 and F18, there is currently no effective vaccine available due to the diversity of these strains. This study presents an innovative approach by isolating chicken-derived single-chain variable fragment antibodies (scFvs) specific to K88 and F18 fimbrial antigens from chickens immunized against these ETEC virulence factors. These scFvs effectively inhibited adhesion of K88 and F18 to porcine intestinal epithelial cells (IPEC-J2), with the inhibitory effect demonstrating a dose-dependent increase. Furthermore, a bispecific scFv was designed and expressed in Pichia pastoris. This engineered construct displayed remarkable potency; at a concentration of 25.08 μg, it significantly reduced the adhesion rate of ETEC strains to IPEC-J2 cells by 72.10% and 69.11% when challenged with either K88 or F18 alone. Even in the presence of both antigens, the adhesion rate was notably decreased by 57.92%. By targeting and impeding the initial adhesion step of ETEC pathogenesis, this antibody-based intervention holds promise as a potential alternative to antibiotics, thereby mitigating the risks associated with antibiotic resistance and residual drug contamination in livestock production. Overall, this study lays the groundwork for the development of innovative treatments against ETEC infections in piglets.

The Humanization and Maturation of an Anti-PrPc Antibody

The cellular prion protein (PrPc) is a cell surface glycoprotein that is highly expressed in a variety of cancer tissues in addition to the nervous system, and its elevated expression is correlated to poor prognosis in many cancer patients. Our team previously found that patients with colorectal cancer (CRC) with high-level PrPc expression had significantly poorer survival than those with no or low-level PrPc expression. Mouse antibodies for PrPc inhibited tumor initiation and liver metastasis of PrPc-positive human CRC cells in mouse model experiments. PrPc is a candidate target for CRC therapy. In this study, we newly cloned a mouse anti-PrPc antibody (Clone 6) and humanized it, then affinity-matured this antibody using a CHO cell display with a peptide antigen and full-length PrPc, respectively. We obtained two humanized antibody clones with affinities toward a full-length PrPc of about 10- and 100-fold of that of the original antibody. The two humanized antibodies bound to the PrPc displayed significantly better on the cell surface than Clone 6. Used for Western blotting and immunohistochemistry, the humanized antibody with the highest affinity is superior to the two most frequently used commercial antibodies (8H4 and 3F4). The two new antibodies have the potential to be developed as useful reagents for PrPc detection and even therapeutic antibodies targeting PrPc-positive cancers.

Molecular and structural basis of anti-DNA antibody specificity for pyrrolated proteins

Anti-DNA antibodies (Abs), serological hallmarks of systemic lupus erythematosus (SLE) and markers for diagnosis and disease activity, show a specificity for non-nucleic acid molecules, such as N-pyrrolated proteins (pyrP) containing Nε-pyrrole-L-lysine (pyrK) residues. However, the detailed mechanism for the binding of anti-DNA Abs to pyrP remains unknown. In the present study, to gain structural insights into the dual-specificity of anti-DNA Abs, we used phage display to obtain DNA-binding, single-chain variable fragments (scFvs) from SLE-prone mice and found that they also cross-reacted with pyrP. It was revealed that a variable heavy chain (VH) domain is sufficient for the recognition of DNA/pyrP. Identification of an antigenic sequence containing pyrK in pyrP suggested that the presence of both pyrK and multiple acidic amino acid residues plays important roles in the electrostatic interactions with the Abs. X-ray crystallography and computer-predicted simulations of the pyrK-containing peptide-scFv complexes identified key residues of Abs involved in the interaction with the antigens. These data provide a mechanistic insight into the molecular basis of the dual-specificity of the anti-DNA Abs and provide a basis for therapeutic intervention against SLE.

Computational Analysis of B-Cell Receptor (BCR) Immune Repertoires with Abalign

The widespread application of high-throughput sequencing technology has generated massive sequences of B-cell receptor (BCR) immune repertoires. Computational analysis of these data has gained significant attention due to the increasing importance of immunotherapy and precision medicine. It not only reveals the diversity and dynamic changes in immune responses, contributing to the study of associated diseases, but also provides valuable information for immunodiagnostics and drug development. Recently, we introduced a BCR-specific multiple sequence alignment (MSA) method along with a comprehensive platform software called Abalign, which stands out as an excellent choice for analyzing BCR immune repertoires due to its unique high-throughput processing capability. It offers ultra-fast MSA functionality and a wide range of analytical features, including BCR/antibody extraction, clonal grouping, lineage tree construction, mutation profiling, diversity statistics, VJ gene assignment, antibody humanization, and more. Importantly, users can perform these analyses using the graphical user interface without any programming skills or scripts. In this article, we present a series of protocols that integrate Abalign's analysis modules into a cohesive workflow. This step-by-step workflow provides detailed instructions for software installation, data preparation, and comprehensive analysis of BCR immune repertoires. This workflow facilitates the efficient acquisition of comprehensive results in profiling BCR immune repertoires, offering insights into the impacts of infectious diseases, allergies, autoimmune disorders, tumor immunology, and antibody drugs. Abalign is freely available at http://cao.labshare.cn/abalign/. © 2024 Wiley Periodicals LLC. Basic Protocol 1: Resource preparation Basic Protocol 2: Analyzing BCR immune repertoires Support Protocol 1: Aiding antibody humanization Support Protocol 2: Constructing B-cell lineage trees Alternate Protocol: Running with Linux command line Basic Protocol 3: Comparing BCR immune repertoires.

The origins of nonideality exhibited by monoclonal antibodies and Fab fragments in human serum

The development of therapeutic antibodies remains challenging, time-consuming, and expensive. A key contributing factor is a lack of understanding of how proteins are affected by complex biological environments such as serum and plasma. Nonideality due to attractive or repulsive interactions with cosolutes can alter the stability, aggregation propensity, and binding interactions of proteins in solution. Fluorescence correlation spectroscopy (FCS) can be used to measure apparent second virial coefficient (B2,app ) values for therapeutic and model monoclonal antibodies (mAbs) that capture the nature and strength of interactions with cosolutes directly in undiluted serum and similar complex biological media. Here, we use FCS-derived B2,app measurements to identify the components of human serum responsible for nonideal interactions with mAbs and Fab fragments. Most mAbs exhibit neutral or slightly attractive interactions with intact serum. Generally, mAbs display repulsive interactions with albumin and mildly attractive interactions with IgGs in the context of whole serum. Crucially, however, these attractive interactions are much stronger with pooled IgGs isolated from other serum components, indicating that the effects of serum nonideality can only be understood by studying the intact medium (rather than isolated components). Moreover, Fab fragments universally exhibited more attractive interactions than their parental mAbs, potentially rendering them more susceptible to nonideality-driven perturbations. FCS-based B2,app measurements have the potential to advance our understanding of how physiological environments impact protein-based therapeutics in general. Furthermore, incorporating such assays into preclinical biologics development may help de-risk molecules and make for a faster and more efficient development process.

In Vitro Affinity Maturation of Nanobodies against Mpox Virus A29 Protein Based on Computer-Aided Design

Mpox virus (MPXV), the most pathogenic zoonotic orthopoxvirus, caused worldwide concern during the SARS-CoV-2 epidemic. Growing evidence suggests that the MPXV surface protein A29 could be a specific diagnostic marker for immunological detection. In this study, a fully synthetic phage display library was screened, revealing two nanobodies (A1 and H8) that specifically recognize A29. Subsequently, an in vitro affinity maturation strategy based on computer-aided design was proposed by building and docking the A29 and A1 three-dimensional structures. Ligand-receptor binding and molecular dynamics simulations were performed to predict binding modes and key residues. Three mutant antibodies were predicted using the platform, increasing the affinity by approximately 10-fold compared with the parental form. These results will facilitate the application of computers in antibody optimization and reduce the cost of antibody development; moreover, the predicted antibodies provide a reference for establishing an immunological response against MPXV.

Antibody designing against IIIabc junction (JIIIabc) of HCV IRES through affinity maturation; RNA-Antibody docking and interaction analysis

Hepatitis C virus is a single-stranded RNA based virus which can cause chronic HCV and hepatocellular carcinoma. HCV genotype 3a has relatively higher rate of fibrosis progression, prevalence of steatosis and incidence of HCC. Despite HCVs variation in genomic sequence, the 5' untranslated region containing internal ribosome entry site (IRES) is highly conserved among all genotypes. It is responsible for translation and initiation of the viral protein. In present study, IRES was targeted by designing variants of reported antigen binding fragment (Fab) through affinity maturation approach. Affinity maturation strategy allowed the rational antibody designing with better biophysical properties and antibody-antigen binding interactions. Complementarity determining regions of reported Fab (wild type) were assessed and docked with IRES. Best generated model of Fab was selected and subjected to alanine scanning Three sets of insilico mutations for variants (V) designing were selected; single (1-71), double (a-j) and triple (I-X). Redocking of IRES-Fab variants consequently enabled the discovery of three variants exhibiting better docking score as compared to the wild type Fab. V1, V39 and V4 exhibited docking scores of -446.51, -446.52 and-446.29 kcal/mol respectively which is better as compared to the wild type Fab that exhibited the docking score of -351.23 kcal/mol. Variants exhibiting better docking score were screened for aggregation propensity by assessing the aggregation prone regions in Fab structure. Total A3D scores of wild type Fab, V1, V4 and V39 were predicted as -315.325, -312.727, -316.967 and -317.545 respectively. It is manifested that solubility of V4 and V39 is comparable to wild type Fab. In future, development and invitro assessment of these promising Fab HCV3 variants is aimed.

Abalign: a comprehensive multiple sequence alignment platform for B-cell receptor immune repertoires

The utilization of high-throughput sequencing (HTS) for B-cell receptor (BCR) immune repertoire analysis has become widespread in the fields of adaptive immunity and antibody drug development. However, the sheer volume of sequences generated by these experiments presents a challenge in data processing. Specifically, multiple sequence alignment (MSA), a critical aspect of BCR analysis, remains inadequate for handling massive BCR sequencing data and lacks the ability to provide immunoglobulin-specific information. To address this gap, we introduce Abalign, a standalone program specifically designed for ultrafast MSA of BCR/antibody sequences. Benchmark tests demonstrate that Abalign achieves comparable or even better accuracy than state-of-the-art MSA tools, and shows remarkable advantages in terms of speed and memory consumption, reducing the time required for high-throughput analysis from weeks to hours. In addition to its alignment capabilities, Abalign offers a broad range of BCR analysis features, including extracting BCRs, constructing lineage trees, assigning VJ genes, analyzing clonotypes, profiling mutations, and comparing BCR immune repertoires. With its user-friendly graphic interface, Abalign can be easily run on personal computers instead of computing clusters. Overall, Abalign is an easy-to-use and effective tool that enables researchers to analyze massive BCR/antibody sequences, leading to new discoveries in the field of immunoinformatics. The software is freely available at http://cao.labshare.cn/abalign/.

Structural insights into the broad protection against H1 influenza viruses by a computationally optimized hemagglutinin vaccine

Influenza virus poses an ongoing human health threat with pandemic potential. Due to mutations in circulating strains, formulating effective vaccines remains a challenge. The use of computationally optimized broadly reactive antigen (COBRA) hemagglutinin (HA) proteins is a promising vaccine strategy to protect against a wide range of current and future influenza viruses. Though effective in preclinical studies, the mechanistic basis driving the broad reactivity of COBRA proteins remains to be elucidated. Here, we report the crystal structure of the COBRA HA termed P1 and identify antigenic and glycosylation properties that contribute to its immunogenicity. We further report the cryo-EM structure of the P1-elicited broadly neutralizing antibody 1F8 bound to COBRA P1, revealing 1F8 to recognize an atypical receptor binding site epitope via an unexpected mode of binding.

Generation of a novel antibody against BxPrx, a diagnostic marker of pine wilt disease

BACKGROUND

Bursaphelenchus xylophilus is a pathogenic nematode that causes pine wilt disease (PWD). To prevent the rapid spread of this pathogen, developing a method for rapid and accurate detection of B. xylophilus is required.

METHODS AND RESULTS

In this study, we produced a B. xylophilus peroxiredoxin (BxPrx), which is a protein that is overexpressed in B. xylophilus. Using recombinant BxPrx as an antigen, we generated and selected a novel antibody that binds to BxPrx via phage display and biopanning. We subcloned the anti-BxPrx single-chain variable fragment-encoding phagemid DNA to mammalian expression vector. We transfected the plasmid into mammalian cells and produced a highly sensitive recombinant antibody that enabled nanogram order detection of BxPrx.

CONCLUSION

The sequence of anti-BxPrx antibody as well as the rapid immunoassay system described here can be applied for rapid and accurate diagnosis of PWD.

Significance of antibody numbering systems in the development of antibody engineering

Immunotherapy has become increasingly popular in recent years for treating a variety of diseases including inflammatory, neurological, oncological, and auto-immune disorders. The significant interest in antibody development is due to the high binding affinity and specificity of an antibody against a specific antigen. Recent advances in antibody engineering have provided a different view on how to engineer antibodies in silico for therapeutic and diagnostic applications. In order to improve the clinical utility of therapeutic antibodies, it is of paramount importance to understand the various molecular properties which impact antigen targeting and its potency. In antibody engineering, antibody numbering (AbN) systems play an important role to identify the complementarity determining regions (CDRs) and the framework regions (FR). Hence, it is crucial to accurately define and understand the CDR, FR and the crucial residues of heavy and light chains that aid in the binding of the antibody to the antigenic site. Detailed understanding of amino acids positions are useful for modifying the binding affinity, specificity, physicochemical features, and half-life of an antibody. In this review, we have summarized the different antibody numbering systems that are widely used in antibody engineering and highlighted their significance. Here, we have systematically explored and mentioned the various tools and servers that harness different AbN systems.

Prediction of polyreactive and nonspecific single-chain fragment variables through structural biochemical features and protein language-based descriptors

BACKGROUND

Monoclonal antibodies (mAbs) have been used as therapeutic agents, which must overcome many developability issues after the discovery from in vitro display libraries. Especially, polyreactive mAbs can strongly bind to a specific target and weakly bind to off-target proteins, which leads to poor antibody pharmacokinetics in clinical development. Although early assessment of polyreactive mAbs is important in the early discovery stage, experimental assessments are usually time-consuming and expensive. Therefore, computational approaches for predicting the polyreactivity of single-chain fragment variables (scFvs) in the early discovery stage would be promising for reducing experimental efforts.

RESULTS

Here, we made prediction models for the polyreactivity of scFvs with the known polyreactive antibody features and natural language model descriptors. We predicted 19,426 protein structures of scFvs with trRosetta to calculate the polyreactive antibody features and investigated the classifying performance of each factor for polyreactivity. In the known polyreactive features, the net charge of the CDR2 loop, the tryptophan and glycine residues in CDR-H3, and the lengths of the CDR1 and CDR2 loops, importantly contributed to the performance of the models. Additionally, the hydrodynamic features, such as partial specific volume, gyration radius, and isoelectric points of CDR loops and scFvs, were newly added to improve model performance. Finally, we made the prediction model with a robust performance ([Formula: see text]) with an ensemble learning of the top 3 best models.

CONCLUSION

The prediction models for polyreactivity would help assess polyreactive scFvs in the early discovery stage and our approaches would be promising to develop machine learning models with quantitative data from high throughput assays for antibody screening.

The applications of machine learning in HIV neutralizing antibodies research-A systematic review

Machine learning algorithms play an essential role in bioinformatics and allow exploring the vast and noisy biological data in unrivaled ways. This paper is a systematic review of the applications of machine learning in the study of HIV neutralizing antibodies. This significant and vast research domain can pave the way to novel treatments and to a vaccine. We selected the relevant papers by investigating the available literature from the Web of Science and PubMed databases in the last decade. The computational methods are applied in neutralization potency prediction, neutralization span prediction against multiple viral strains, antibody-virus binding sites detection, enhanced antibodies design, and the study of the antibody-induced immune response. These methods are viewed from multiple angles spanning data processing, model description, feature selection, evaluation, and sometimes paper comparisons. The algorithms are diverse and include supervised, unsupervised, and generative types. Both classical machine learning and modern deep learning were taken into account. The review ends with our ideas regarding future research directions and challenges.

Engineered fast-dissociating antibody fragments for multiplexed super-resolution microscopy

Image reconstruction by integrating exchangeable single-molecule localization (IRIS) achieves multiplexed super-resolution imaging by high-density labeling with fast exchangeable fluorescent probes. However, previous methods to develop probes for individual targets required a great amount of time and effort. Here, we introduce a method for generating recombinant IRIS probes with a new mutagenesis strategy that can be widely applied to existing antibody sequences. Several conserved tyrosine residues at the base of complementarity-determining regions were identified as candidate sites for site-directed mutagenesis. With a high probability, mutations at candidate sites accelerated the off rate of recombinant antibody-based probes without compromising specific binding. We were able to develop IRIS probes from five monoclonal antibodies and three single-domain antibodies. We demonstrate multiplexed localization of endogenous proteins in primary neurons that visualizes small synaptic connections with high binding density. It is now practically feasible to generate fast-dissociating fluorescent probes for multitarget super-resolution imaging.

Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning

MOTIVATION

Knowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivity can be used to find potent antibody combinations that broadly neutralize multiple and diverse HIV strains. Sensitivity prediction can be combined with other methods such as generative algorithms to design novel antibodies in silico or with feature selection to uncover the sites of interest in the sequence. However, these tools are limited in the absence of in silico accurate prediction methods.

RESULTS

Our method leverages the CATNAP dataset, probably the most comprehensive collection of HIV-antibodies assays, and predicts the antibody-virus sensitivity in the form of binary classification. The methods proposed by others focus primarily on analyzing the virus sequences. However, our article demonstrates the advantages gained by modeling the antibody-virus sensitivity as a function of both virus and antibody sequences. The input is formed by the virus envelope and the antibody variable region aminoacid sequences. No structural features are required, which makes our system very practical, given that sequence data is more common than structures. We compare with two other state-of-the-art methods that leverage the same dataset and use sequence data only. Our approach, based on neuronal networks and transfer learning, measures increased predictive performance as measured on a set of 31 specific broadly neutralizing antibodies.

AVAILABILITY AND IMPLEMENTATION

https://github.com/vlad-danaila/deep_hiv_ab_pred/tree/fc-att-fix.

Comprehensive Characterizations of Immune Receptor Repertoire in Tumors and Cancer Immunotherapy Studies

We applied our computational algorithm TRUST4 to assemble immune receptor (T-cell receptor/B-cell receptor) repertoires from approximately 12,000 RNA sequencing samples from The Cancer Genome Atlas and seven immunotherapy studies. From over 35 million assembled complete complementary-determining region 3 sequences, we observed that the expression of CCL5 and MZB1 is the most positively correlated genes with T-cell clonal expansion and B-cell clonal expansion, respectively. We analyzed amino acid evolution during B-cell receptor somatic hypermutation and identified tyrosine as the preferred residue. We found that IgG1+IgG3 antibodies together with FcRn were associated with complement-dependent cytotoxicity and antibody-dependent cellular cytotoxicity or phagocytosis. In addition to B-cell infiltration, we discovered that B-cell clonal expansion and IgG1+IgG3 antibodies are also correlated with better patient outcomes. Finally, we created a website, VisualizIRR, for users to interactively explore and visualize the immune repertoires in this study. See related Spotlight by Liu and Han, p. 786.

Homogeneous One-Step Immunoassay Based on Switching Peptides for Detection of the Influenza Virus

In this study, a homogeneous one-step immunoassay based on switching peptides is presented for the detection of influenza viruses A and B (Inf-A and Inf-B, respectively). The one-step immunoassay represents an immunoassay method that does not involve any washing steps, only treatment of the sample. In this method, fluorescence-labeled switching peptides quantitatively dissociate from the antigen-binding site of immunoglobulin G (IgG). In particular, the one-step immunoassay based on soluble detection antibodies with switching peptides is called a homogeneous one-step immunoassay. The immunoassay developed uses switching peptides labeled with two types of fluorescence dyes (FAM and TAMRA) and detection antibodies labeled with two types of fluorescence quenchers (TQ2 for FAM and TQ3 for TAMRA). The optimal switching peptides for the detection of Inf-A and Inf-B have been selected as L1-peptide and H2-peptide. The interactions between the four kinds of switching peptides and IgG have been analyzed using computational docking simulation and SPR biosensor. The location of labeling for the fluorescence quenchers has been determined based on the distance between the fluorescence dyes of the switching peptides and the fluorescence quenchers, calculated on the basis of the efficiency of fluorescence quenching, using the Förster equation. To demonstrate the feasibility of the one-step immunoassay, binding constants (K_D) have been calculated for detection antibodies against Inf-A and Inf-B with target antigens (Inf-A and Inf-B) and switching peptides (L1- and H2-peptides), using an isotherm model. The immunoassay has been demonstrated to be feasible using antigens as well as real samples of Inf-A and Inf-B with a critical cycle number (Ct). The immunoassay has also been compared to other commercially available rapid test kits for Inf-A and Inf-B and found to be far more sensitive for detection of Inf-A and Inf-B over the entire detection range.

Antibody engineering improves neutralization activity against K417 spike mutant SARS-CoV-2 variants

BACKGROUND

Neutralizing antibodies are approved drugs to treat coronavirus disease-2019 (COVID-19) patients, yet mutations in severe acute respiratory syndrome coronavirus (SARS-CoV-2) variants may reduce the antibody neutralizing activity. New monoclonal antibodies (mAbs) and antibody remolding strategies are recalled in the battle with COVID-19 epidemic.

RESULTS

We identified multiple mAbs from antibody phage display library made from COVID-19 patients and further characterized the R3P1-E4 clone, which effectively suppressed SARS-CoV-2 infection and rescued the lethal phenotype in mice infected with SARS-CoV-2. Crystal structural analysis not only explained why R3P1-E4 had selectively reduced binding and neutralizing activity to SARS-CoV-2 variants carrying K417 mutations, but also allowed us to engineer mutant antibodies with improved neutralizing activity against these variants. Thus, we screened out R3P1-E4 mAb which inhibits SARS-CoV-2 and related mutations in vitro and in vivo. Antibody engineering improved neutralizing activity of R3P1-E4 against K417 mutations.

CONCLUSION

Our studies have outlined a strategy to identify and engineer neutralizing antibodies against SARS-CoV-2 variants.

SSH2.0: A Better Tool for Predicting the Hydrophobic Interaction Risk of Monoclonal Antibody

Therapeutic antibodies play a crucial role in the treatment of various diseases. However, the success rate of antibody drug development is low partially because of unfavourable biophysical properties of antibody drug candidates such as the high aggregation tendency, which is mainly driven by hydrophobic interactions of antibody molecules. Therefore, early screening of the risk of hydrophobic interaction of antibody drug candidates is crucial. Experimental screening is laborious, time-consuming, and costly, warranting the development of efficient and high-throughput computational tools for prediction of hydrophobic interactions of therapeutic antibodies. In the present study, 131 antibodies with hydrophobic interaction experiment data were used to train a new support vector machine-based ensemble model, termed SSH2.0, to predict the hydrophobic interactions of antibodies. Feature selection was performed against CKSAAGP by using the graph-based algorithm MRMD2.0. Based on the antibody sequence, SSH2.0 achieved the sensitivity and accuracy of 100.00 and 83.97%, respectively. This approach eliminates the need of three-dimensional structure of antibodies and enables rapid screening of therapeutic antibody candidates in the early developmental stage, thereby saving time and cost. In addition, a web server was constructed that is freely available at http://i.uestc.edu.cn/SSH2/.

Dudás E, Fantini M, Puglisi R, Louka A, Marcatili P, Cattaneo A, Pastore A. Selection and Modelling of a New Single-Domain Intrabody Against TDP-43

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder associated to deteriorating motor and cognitive functions, and short survival. The disease is caused by neuronal death which results in progressive muscle wasting and weakness, ultimately leading to lethal respiratory failure. The misbehaviour of a specific protein, TDP-43, which aggregates and becomes toxic in ALS patient’s neurons, is supposed to be one of the causes. TDP-43 is a DNA/RNA-binding protein involved in several functions related to nucleic acid metabolism. Sequestration of TDP-43 aggregates is a possible therapeutic strategy that could alleviate or block pathology. Here, we describe the selection and characterization of a new intracellular antibody (intrabody) against TDP-43 from a llama nanobody library. The structure of the selected intrabody was predicted in silico and the model was used to suggest mutations that enabled to improve its expression yield, facilitating its experimental validation. We showed how coupling experimental methodologies with in silico design may allow us to obtain an antibody able to recognize the RNA binding regions of TDP-43. Our findings illustrate a strategy for the mitigation of TDP-43 proteinopathy in ALS and provide a potential new tool for diagnostics.

Central Nervous System Delivery of Antibodies and Their Single-Domain Antibodies and Variable Fragment Derivatives with Focus on Intranasal Nose to Brain Administration

IgG antibodies are some of the most important biopharmaceutical molecules with a high market volume. In spite of the fact that clinical therapies with antibodies are broadly utilized in oncology, immunology and hematology, their delivery strategies and biodistribution need improvement, their limitations being due to their size and poor ability to penetrate into tissues. In view of their small size, there is a rising interest in derivatives, such as single-domain antibodies and single-chain variable fragments, for clinical diagnostic but also therapeutic applications. Smaller antibody formats combine several benefits for clinical applications and can be manufactured at reduced production costs compared with full-length IgGs. Moreover, such formats have a relevant potential for targeted drug delivery that directs drug cargo to a specific tissue or across the blood–brain barrier. In this review, we give an overview of the challenges for antibody drug delivery in general and focus on intranasal delivery to the central nervous system with antibody formats of different sizes.

CENP-A Nucleosome is a Sensitive Allosteric Scaffold for DNA and Chromatin Factors

Centromeric loci of chromosomes are defined by nucleosomes containing the histone H3 variant CENP-A, which bind their DNA termini more permissively than their canonical counterpart, a feature that is critical for the mitotic fidelity. A recent cryo-EM study demonstrated that the DNA termini of CENP-A nucleosomes, reconstituted with the Widom 601 DNA sequence, are asymmetrically flexible, meaning one terminus is more clearly resolved than the other. However, an earlier work claimed that both ends could be resolved in the presence of two stabilizing single chain variable fragment (scFv) antibodies per nucleosome, and thus are likely permanently bound to the histone octamer. This suggests that the binding of scFv antibodies to the histone octamer surface would be associated with CENP-A nucleosome conformational changes, including stable binding of the DNA termini. Here, we present computational evidence that allows to explain at atomistic level the structural rearrangements of CENP-A nucleosomes resulting from the antibody binding. The antibodies, while they only bind the octamer façades, are capable of altering the dynamics of the nucleosomal core, and indirectly also the surrounding DNA. This effect has more drastic implications for the structure and the dynamics of the CENP-A nucleosome in comparison to its canonical counterpart. Furthermore, we find evidence that the antibodies bind the left and the right octamer façades at different affinities, another manifestation of the DNA sequence. We speculate that the cells could use induction of similar allosteric effects to control centromere function.

Computational approaches to therapeutic antibody design: established methods and emerging trends

Antibodies are proteins that recognize the molecular surfaces of potentially noxious molecules to mount an adaptive immune response or, in the case of autoimmune diseases, molecules that are part of healthy cells and tissues. Due to their binding versatility, antibodies are currently the largest class of biotherapeutics, with five monoclonal antibodies ranked in the top 10 blockbuster drugs. Computational advances in protein modelling and design can have a tangible impact on antibody-based therapeutic development. Antibody-specific computational protocols currently benefit from an increasing volume of data provided by next generation sequencing and application to related drug modalities based on traditional antibodies, such as nanobodies. Here we present a structured overview of available databases, methods and emerging trends in computational antibody analysis and contextualize them towards the engineering of candidate antibody therapeutics.